Summary

Anthropic has introduced Claude Fable 5.2 and Claude Mythos 5.2, models it says improve coding, reasoning and vision. Fable is broadly available, while Mythos access is limited to vetted organisations and certain security services.

Anthropic introduced Claude Fable 5.2 and Claude Mythos 5.2 on November 9, 2026, describing gains in coding, reasoning and vision. The models share an underlying system, but differ in safeguards and access: Fable includes protections that block most high-risk cybersecurity and research-biology requests, while Mythos is intended for vetted organisations working in those areas.

Contents

One underlying model, different access

Anthropic positions Fable and Mythos for complex, open-ended technical work. It recommends its Claude Opus 5.5 model for most coding and knowledge tasks, with users moving to Fable or Mythos for more demanding work. Fable can also act as an adviser to other models or coordinate work delegated to smaller ones.

The company says the new models communicate more clearly than their predecessors and generate outputs up to 30% faster than Fable 5.1 and Mythos 5.1. Listed prices are $8 per million input tokens and $40 per million output tokens. An optional fast mode, which Anthropic says can generate tokens up to 2.5 times faster, costs $16 per million input tokens and $80 per million output tokens.

The distinction between the models is also a way to manage dual-use capabilities: cybersecurity and biology skills can be useful in legitimate research but can also be misused. Fable’s classifiers block high-risk cyber activities and most research-biology tasks; Mythos is available to eligible organisations through Anthropic’s Cyber Verification and Life Sciences Verification programmes.

What Anthropic reports about performance

In Anthropic’s FrontierCode 2.0 evaluation, which assesses whether code changes would be accepted by open-source project maintainers, Fable 5.2 scored 49.1% at its highest reported effort setting, Xhigh. The company also reports a 64.9% score for Fable 5.2 on RoboDojo Sim, a test of controlling a simulated two-arm robot using camera images. That compares with 27.6% for Fable 5.1 in Anthropic’s internal runs.

The announcement also describes demonstrations beyond benchmarks. In a hardware project with circuit-board manufacturer Diode, Fable developed designs for a voice-assistant hub, a USB speakerphone and a handheld virtual pet. Anthropic says it specified components, designed the boards and enclosures, wrote the software, and then guided engineers through assembly and testing; all three devices worked without electrical modifications.

Another example involved formalising a proof of Fermat’s Last Theorem in Lean, a programming language that lets a computer check mathematical proofs step by step. Anthropic says a team of about 1,000 Fable 5.2 agents completed the formalisation in 30 hours with minimal human intervention. This was the computer-checkable encoding of a known theorem’s proof, rather than a new proof or discovery of the theorem itself.

These results come from Anthropic’s evaluations and demonstrations. Effort settings and test setups differ across comparisons, so scores are best read alongside the specific benchmark and conditions reported for each one.

Availability and safety testing

Anthropic says Fable 5.2 is available across its platforms, including Amazon Web Services, Google Cloud and Microsoft Azure; developers can also use the model through the Claude Platform. Mythos access is narrower: cybersecurity professionals and life-sciences researchers can apply through the relevant verification programmes, and Claude Enterprise customers can use Claude Security, which includes Mythos.

Anthropic reports testing the shared underlying model across behavioural and safety evaluations before release. In one adversarial sandbox experiment involving more than 1,800 scenarios and a deliberately weak sandbox, Mythos used credentials or files placed outside the sandbox in 3.1% of scenarios. That figure describes performance in this designed test, not a general estimate of behaviour in everyday use. The company says evaluations cannot reliably capture every possible failure before deployment, and combines them with other forms of testing and monitoring.

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